





"""simple docstring"""





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = (EulerDiscreteScheduler,)
      _SCREAMING_SNAKE_CASE							      = 10





      def        _snake_case     (			self ,		**lowercase					)						->	Union[str, Any]:
            lowerCAmelCase    		=			{
                """num_train_timesteps""": 1_100,
                """beta_start""": 0.0_001,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
            }

            config.update(**lowercase					)
            return config





      def        _snake_case     (			self					)						->	Optional[int]:
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=lowercase					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,		[0.0_002, 0.002, 0.02]					):
                  self.check_over_configs(beta_start=lowercase ,		beta_end=lowercase					)





      def        _snake_case     (			self					)						->	Dict:
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=lowercase					)





      def        _snake_case     (			self					)						->	Optional[Any]:
            for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.scheduler_classes[0]
            lowerCAmelCase    		=			self.get_scheduler_config()
            lowerCAmelCase    		=			scheduler_class(**lowercase					)

            scheduler.set_timesteps(self.num_inference_steps					)

            lowerCAmelCase    		=			torch.manual_seed(0					)

            lowerCAmelCase    		=			self.dummy_model()
            lowerCAmelCase    		=			self.dummy_sample_deter * scheduler.init_noise_sigma
            lowerCAmelCase    		=			sample.to(lowercase					)

            for i, t in enumerate(scheduler.timesteps					):
                  lowerCAmelCase    		=			scheduler.scale_model_input(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			model(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			scheduler.step(lowercase ,		lowercase ,		lowercase ,		generator=lowercase					)
                  lowerCAmelCase    		=			output.prev_sample

            lowerCAmelCase    		=			torch.sum(torch.abs(lowercase					)					)
            lowerCAmelCase    		=			torch.mean(torch.abs(lowercase					)					)

            assert abs(result_sum.item() - 10.0_807					) < 1e-2
            assert abs(result_mean.item() - 0.0_131					) < 1e-3





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.scheduler_classes[0]
            lowerCAmelCase    		=			self.get_scheduler_config(prediction_type="""v_prediction"""					)
            lowerCAmelCase    		=			scheduler_class(**lowercase					)

            scheduler.set_timesteps(self.num_inference_steps					)

            lowerCAmelCase    		=			torch.manual_seed(0					)

            lowerCAmelCase    		=			self.dummy_model()
            lowerCAmelCase    		=			self.dummy_sample_deter * scheduler.init_noise_sigma
            lowerCAmelCase    		=			sample.to(lowercase					)

            for i, t in enumerate(scheduler.timesteps					):
                  lowerCAmelCase    		=			scheduler.scale_model_input(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			model(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			scheduler.step(lowercase ,		lowercase ,		lowercase ,		generator=lowercase					)
                  lowerCAmelCase    		=			output.prev_sample

            lowerCAmelCase    		=			torch.sum(torch.abs(lowercase					)					)
            lowerCAmelCase    		=			torch.mean(torch.abs(lowercase					)					)

            assert abs(result_sum.item() - 0.0_002					) < 1e-2
            assert abs(result_mean.item() - 2.2676e-06					) < 1e-3





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.scheduler_classes[0]
            lowerCAmelCase    		=			self.get_scheduler_config()
            lowerCAmelCase    		=			scheduler_class(**lowercase					)

            scheduler.set_timesteps(self.num_inference_steps ,		device=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)

            lowerCAmelCase    		=			self.dummy_model()
            lowerCAmelCase    		=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase    		=			sample.to(lowercase					)

            for t in scheduler.timesteps:
                  lowerCAmelCase    		=			scheduler.scale_model_input(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			model(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			scheduler.step(lowercase ,		lowercase ,		lowercase ,		generator=lowercase					)
                  lowerCAmelCase    		=			output.prev_sample

            lowerCAmelCase    		=			torch.sum(torch.abs(lowercase					)					)
            lowerCAmelCase    		=			torch.mean(torch.abs(lowercase					)					)

            assert abs(result_sum.item() - 10.0_807					) < 1e-2
            assert abs(result_mean.item() - 0.0_131					) < 1e-3





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			self.scheduler_classes[0]
            lowerCAmelCase    		=			self.get_scheduler_config()
            lowerCAmelCase    		=			scheduler_class(**lowercase ,		use_karras_sigmas=lowercase					)

            scheduler.set_timesteps(self.num_inference_steps ,		device=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)

            lowerCAmelCase    		=			self.dummy_model()
            lowerCAmelCase    		=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase    		=			sample.to(lowercase					)

            for t in scheduler.timesteps:
                  lowerCAmelCase    		=			scheduler.scale_model_input(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			model(lowercase ,		lowercase					)

                  lowerCAmelCase    		=			scheduler.step(lowercase ,		lowercase ,		lowercase ,		generator=lowercase					)
                  lowerCAmelCase    		=			output.prev_sample

            lowerCAmelCase    		=			torch.sum(torch.abs(lowercase					)					)
            lowerCAmelCase    		=			torch.mean(torch.abs(lowercase					)					)

            assert abs(result_sum.item() - 124.52_299_499_511_719					) < 1e-2
            assert abs(result_mean.item() - 0.16_213_932_633_399_963					) < 1e-3













"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			""""""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[chr(i + 65     ) for i in range(26     )]
      # Remove duplicate characters from key
      lowerCAmelCase    		=			remove_duplicates(key.upper()     )
      lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     )
      # First fill cipher with key characters
      lowerCAmelCase    		=			{alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE     )}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(SCREAMING_SNAKE_CASE     )				,		26     ):
            lowerCAmelCase    		=			alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  lowerCAmelCase    		=			alphabet[i - offset]
            lowerCAmelCase    		=			char
      return cipher_alphabet


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      return "".join(cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			input("""Enter message to encode or decode: """     ).strip()
      lowerCAmelCase    		=			input("""Enter keyword: """     ).strip()
      lowerCAmelCase    		=			input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
      try:
            lowerCAmelCase    		=			{"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""     )
      lowerCAmelCase    		=			create_cipher_map(SCREAMING_SNAKE_CASE     )
      print(func(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()






